For WMF [1]}, LLORMA [2]}, [3]}, and sGLSVD [4]}, we tuned the \(\lambda _{u}\)  and \(\lambda _{i}\)  among {1, 10, 100} and chose the \(\alpha \)  among {2, 5, 10}. For AE-based models, we set the dimension of the latent representation to 200 and tuned the dropout ratio among {0, 0.2, 0.5}. For MultVAE [5]}, we did not apply the \(L_2\) -norm regularization and used either 0 or 1 additional MLP layers, as discussed in [5]}. For EASE\(^{R}\)  [7]}, we tuned the \(L_2\) -norm regularization parameter \(\lambda \)  among {1, 10, 100, 1000}. For RecVAE [8]}, we tuned the \(\beta \)  among {0.1, 0.01, 0.001}. For LLORMA [2]}, [3]}, we set the number of local models as 100 and tuned the kernel bandwidth \(h\)  = {0.8, 1.0, 2.0, 4.0, 5.0}. For sGLSVD [4]}, we tuned the number of local models using the clustering algorithm among {2, 3, 5, 10, 20, 50, 100}, as discussed in [12]}. For LOCA, we tuned the training kernel bandwidth \(h_T\)  in \([0.8, 2.0]\)  and aggregating bandwidth \(h_W\)  in \([0.2, 0.8]\) .
